OBJECTIVES Health-behaviour change is not predicted well by behavioural intentions alone. Action planning and coping planning are two self-regulatory strategies that may help bridge the gap between intentions and behaviour. Action planning comprises the when, where and how of implementing a behaviour. Coping planning involves the anticipation of barriers and ways to overcome them. The study exa...

Three experiments investigated the impact of planning and preparing a manual grasping or pointing movement on feature detection in a visual search task. The authors hypothesized that action planning may prime perceptual dimensions that provide information for the open parameters of that action. Indeed, preparing for grasping facilitated detection of size targets while preparing for pointing fac...

In recent years, voltage limit violation and power system load-generation imbalance, i.e., line loading limit violation have been responsible for several incidents of major network collapses leading to partial or even complete blackouts. Alleviation of line overloads is the suitable corrective action in this regard. The control action strategies to limit the line loading to the security limits ...

Autonomy requires robots to learn models of the environment or objects while simultaneously searching for solutions in the partially observable state space. A flexible representation that supports incremental acquisition of models has several advantages for solving such tasks. In this paper, we investigate the use of graphs to capture the interaction statistics of an agent with aspects of the e...

Graphs are suitable modeling formalisms for software and hardware systems involving aspects such as communication, object orientation, concurrency, mobility and distribution. State spaces of such systems can be represented by graph transition systems, which are basically transition systems whose states and transitions represent graphs and graph morphisms. In this paper, we propose the modeling ...

We question the generalizability of Glover’s model because it fails to distinguish between different forms of planning. The highly controlled experimental situations on which this model is based, do not reflect some important factors that contribute to planning. We discuss several classes of action that seem to imply distinct planning mechanisms, questioning Glover’s postulation of a single “pl...

AI planning techniques often require a given set of action models provided as input. Creating action models is, however, a difficult task that costs much manual effort. The problem of action-model acquisition has drawn a lot of interest from researchers in the past. Despite the success of the previous systems, they are all based on the assumption that there are enough training examples for lear...

This is a discussion of some of the achievements and challenges related to representing actions and the design of planners from the perspective of logic programming. We talk about recent work on action languages and translating them into logic programming, on representing possible histories of an action domain by answer sets, on eecient implementations of the answer set semantics and their use ...

In this paper we translate concurrent C/C++ code into PDDL. The system then runs heuristic search planners against the PDDL outcome to generate traces for locating programming bugs. These counter-examples result in an interactive debugging aid and exploit efficient planner in-built heuristics. Different aspects like parsing, generation of the dependency graph, slicing, abstraction, and property...

AI planning requires action models to be given in advance. However, it is both time consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL, as a result, learning action models is important for AI planning. On the other hand, the data being used to learn action models are often limited in planning domains, which makes the learning task very dif...